Model-to-model data assimilation method for fine resolution ocean modelling

Abstract. An effective and computationally efficient method is presented for data assimilation in a high-resolution (child) ocean model, which is nested into a coarse-resolution good quality data assimilating (parent) model. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) reduces bias and root mean square errors (RMSE) of the child model and does not allow the child model to drift away from reality. The basic idea is to assimilate data from the parent model instead of actual observations. In this way, the child model is physically aware of observations via the parent model. The method allows to avoid a complex process of assimilating the same observations which were already assimilated into the parent model. The method consists of two stages: (1) downscaling the parent model output onto the child model grid using Stochastic-Deterministic Downscaling, and (2) applying a simplified Kalman gain formula to each of the fine grid nodes. The method is illustrated in a synthetic case where the true solution is known, and the child model forecast (before data assimilation) is simulated by adding various types of errors. The SDDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE typically by a factor of 2 to 5.